Given spatial data samples with explanatory features and a targeted response variable at different locations and time, the spatial and spatiotemporal prediction problem aims to learn a model that can predict the response variable based on their explanatory features. Examples of the problem existed even before the popularity of computers. The spatial interpolation method Kriging, named after spatial statistician and mining engineer Krige [1]}, was developed in the 1960s to estimate minerals for mining activities. However, with the recent advancement of GPS and remote sensing technology, as well as the popularity of geographic information system and mobile devices, large amounts of spatial and spaiotemporal data are being collected at an increasing speed, such as earth observation imagery, gauge observations in river streams, and geo-social media data. Utilizing the rich geospatial data plays a critical role in addressing many grand societal issues, but is also technically challenging due to the unique characteristics of spatial data. There is an urgent need for effective and efficient prediction methods that can unlock the values of such rich geospatial data assets.
